Enhancing email classification using data reduction and disagreement-based semi-supervised learning

Email classification is an important topic in literature attempting to correctly classify user emails and filter out spam emails. In this paper, we identify some challenges regarding this topic and propose an effective email classification model based on both data reduction and disagreement-based se...

Full description

Saved in:
Bibliographic Details
Published in2014 IEEE International Conference on Communications (ICC) pp. 622 - 627
Main Authors Yuxin Meng, Wenjuan Li, Lam-For Kwok
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2014
Subjects
Accuracy
Data models
Data Reduction
Disagreement-based Semi-Supervised Learning
Electronic mail
Email Classification
Machine Learning
Semisupervised learning
Support vector machines
Training
Vegetation
Online AccessGet full text

Cover

More Information
Summary:Email classification is an important topic in literature attempting to correctly classify user emails and filter out spam emails. In this paper, we identify some challenges regarding this topic and propose an effective email classification model based on both data reduction and disagreement-based semi-supervised learning. In particular, the main objective of the data reduction is to select an optimum collection of email features and reduce the pointless data, while the objective of the disagreement-based approach is to enhance the accuracy of detecting spam emails by utilizing unlabeled data automatically. In the evaluation, we explore the performance of our proposed email classification model using two public datasets and a private dataset. The experimental results demonstrate that our proposed model can overall enhance the performance of email classification through improving detection accuracy and reducing false rates.
ISSN:1550-3607
1938-1883
DOI:10.1109/ICC.2014.6883388